
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "129"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 80 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 76 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	0.00%	2.174937	-0.882275	1.363917	-1.068189	0.157452	-1.067741	1.388302	-1.195665	0.7470	0.7718	0.2645	3.574472	3.473473
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.00%	0.844783	-0.160063	1.355609	-0.836285	0.171246	-0.423407	-0.351372	-1.150308	0.6769	0.6735	0.2473	2.342238	2.413399
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.337155	3.769122	34.688060	37.983661	29.527304	32.713086	1.658397	3.681882	0.7109	0.7212	0.3707	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.699055	-0.526185	6.871447	3.200529	-0.280153	-0.413880	-0.059478	-0.695355	0.6068	0.6242	0.3503	7.633599	7.725727
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.823660	-0.046566	2.513845	-0.508715	0.123752	1.544423	0.159256	-0.778897	0.6113	0.6257	0.3465	2.033665	1.989218
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	1.08%	0.650843	-0.843989	2.355638	-0.200078	0.033130	-0.708215	0.356618	-0.779975	0.6588	0.6600	0.2796	1.905224	1.961039
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	199.611762	221.473010	inf	inf	21.813919	26.091650	48.820385	18.174900	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	0.00%	-	-	-	2.80%	0.00%	1.080781	-0.277035	3.023248	-0.949664	1.409515	-0.548533	0.845631	-0.100814	nan	nan	nan	0.821172	0.850101
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.347205	0.818913	4.048997	2.838333	6.034178	5.892840	1.334591	0.031361	0.6208	0.6386	0.3274	6.141622	5.948094
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.762136	36.421572	14.023817	15.042453	4.206613	3.495369	5.502094	6.714242	0.6785	0.6770	0.2908	0.000000	0.000000
2459584	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.60%	1.400678	0.403874	-0.801318	0.047472	1.073662	-2.243375	-0.675353	-0.911894	0.5644	0.5805	0.3428	1.195075	1.172564
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.682473	-0.968721	-0.846653	0.616970	0.942988	0.100843	-0.681294	-0.727202	0.6163	0.6325	0.3626	1.815736	1.804240
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	2.70%	0.00%	-0.023989	-0.505254	0.828590	2.353190	-0.040074	0.006147	-0.710234	-0.597736	0.6241	0.6380	0.3624	1.823746	1.779906
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	0.00%	0.490064	-0.399032	-0.157823	0.360265	-0.198436	0.925066	-0.593527	-0.743673	0.6177	0.6333	0.3719	1.477180	1.375943
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	0.241275	-0.343688	0.997330	1.766797	0.546265	3.000209	-0.570805	-0.667099	0.6284	0.6429	0.3609	1.751748	1.686290
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-0.425273	-0.259836	-0.465380	-0.074642	0.219126	-0.024040	-0.644171	-0.577460	0.6241	0.6391	0.3672	1.877572	1.879460
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.797094	4.445268	20.723314	21.652720	30.934687	44.601685	23.437848	29.594950	0.7382	0.7435	0.3879	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.413208	-0.010835	1.537410	0.999711	-0.441737	-0.404853	-0.537241	-0.612310	0.6176	0.6345	0.3697	1.403898	1.362788
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	8.06%	1.248392	-0.139220	1.325860	1.129960	-0.275579	-0.389759	-0.199074	-0.100806	0.6472	0.6637	0.3653	1.713960	1.774162
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	4.84%	0.559153	-0.727734	0.697086	0.655080	0.221288	-0.156917	1.006243	0.737521	0.7240	0.7312	0.3104	1.705562	1.855485
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	2.70%	0.632571	-0.093636	-0.325668	-0.507899	-0.694118	-1.320420	-0.602248	-0.882968	0.6438	0.6594	0.3689	1.709391	1.759984
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	2.70%	0.422792	0.085468	-0.602406	-0.851211	0.381373	0.000669	-0.688822	-0.520052	0.6378	0.6584	0.3671	1.687574	1.718805
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.104241	-0.515221	0.981636	0.914218	-0.690591	-0.738071	-0.018678	-0.048036	0.6391	0.6586	0.3674	1.457761	1.484099
2459571	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.062092	4.308973	21.086762	22.035871	89.596688	105.511085	45.756390	60.805077	0.6684	0.6881	0.3657	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	0.200709	-0.385993	-0.407688	-0.492501	-0.544990	-0.711083	-0.604663	-0.697565	0.7322	0.7435	0.2292	2.534019	2.284234
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.54%	-0.267360	-0.906316	1.115304	1.181157	-0.455727	-0.616147	-0.700846	-0.767060	0.6401	0.6557	0.3717	1.670569	1.687061
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.525629	0.525629	0.479082	0.479082	-0.763628	-0.763628	-0.844260	-0.844260	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.58%	-0.223585	-1.334705	0.079876	0.153764	-0.781431	-0.518303	-0.347695	-0.159995	0.6430	0.6524	0.3759	2.055728	1.766242
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.017638	-0.512846	2.642772	3.072544	0.214334	-0.018377	-0.300609	-0.291288	0.6467	0.6591	0.3662	1.780346	1.780606
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	-0.279899	-0.970192	-0.018167	0.148274	-0.490390	0.103565	-0.065429	-0.055006	0.6463	0.6616	0.3689	1.967897	1.798336
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.111753	-0.736276	0.470230	0.687710	-0.544613	0.021637	0.249590	0.027497	0.6499	0.6697	0.3700	1.762462	1.772169
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	2.16%	0.116902	-0.707792	0.789788	0.749954	-0.342456	-0.020698	0.001368	-0.188479	0.6481	0.6617	0.3738	2.010537	1.797768
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	8.06%	-0.461273	-1.300775	-0.089212	-0.058077	-0.799923	-0.513299	-0.033251	-0.209790	0.6481	0.6630	0.3620	2.022748	1.825461
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	0.072841	-0.358318	0.497686	1.291838	-0.051305	-0.742569	-0.575381	-0.161941	0.6530	0.6661	0.3687	1.728282	1.581220
2459557	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7919	0.7888	0.2970	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.193987	-0.581193	1.167319	1.744891	-0.747354	-0.847415	-0.528883	0.673654	0.6406	0.6559	0.3751	1.718092	1.756136
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6543	0.6667	0.3773	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6504	0.6640	0.3759	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	1.339628	-0.468316	-0.655736	-0.757477	0.225450	-1.448142	-0.824806	-0.823168	0.6524	0.6686	0.3752	0.501916	0.499835
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.230108	-0.865049	0.061486	0.046973	-0.967150	-0.362230	-0.065781	0.025058	0.6394	0.6604	0.3757	1.880505	1.603807
2459550	digital_ok	-	29.65%	29.65%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5047	0.5117	0.2482	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.840773	-0.691469	1.296596	1.389987	-0.024150	-0.797859	-0.413671	-0.549045	0.6435	0.6673	0.3825	1.676101	1.588345
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	1.470755	-0.639956	0.071994	-0.709471	-0.215968	-0.580023	-0.644511	-0.843436	0.7158	0.7279	0.3365	1.456860	1.313615
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	1.368669	-0.563838	1.282484	1.507455	3.168386	1.580255	0.438620	0.457846	0.7004	0.7118	0.3170	1.677861	1.596973
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.45%	0.200545	-0.807029	0.486332	0.624186	1.356876	2.099895	0.026229	0.030594	0.7158	0.7199	0.3277	1.582424	1.584269
2459536	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.208802	-1.314892	0.460085	-0.228594	-0.340594	-1.120449	-0.686282	-1.156940	0.0603	0.0713	0.0074	0.000000	0.000000
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.115513	-1.094294	-0.235010	-0.259311	0.975576	1.708380	-0.296297	-0.492946	0.0671	0.0745	0.0085	50.052142	30.829736
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.714333	-1.159680	0.450458	0.363653	2.315556	2.066457	0.118202	-0.384065	0.0621	0.0730	0.0072	0.000000	0.000000
2459533	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.068700	-0.760616	0.885451	0.948553	0.282261	0.819977	-0.595374	-0.403295	0.0580	0.0716	0.0068	0.000000	0.000000
2459532	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.241264	-0.430422	-0.606411	0.073977	0.237254	0.238361	-0.797910	-0.656255	0.0546	0.0641	0.0047	38.418990	51.751784
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.006902	-1.008411	-0.002711	0.119131	-0.567908	0.100950	-0.165831	-0.264680	0.0621	0.0783	0.0074	28.221594	31.289369
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.133387	-1.212714	-0.570733	-0.750159	0.345431	0.452677	-0.518743	-0.556722	0.9119	0.9390	0.2859	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	6.021900	-1.454103	2.595592	2.633076	1.144448	4.034444	0.415548	0.747281	nan	nan	nan	13.923029	10.200651
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.850868	-0.672081	-0.284954	0.008322	-0.769275	0.823221	-0.425085	-0.337369	0.6359	0.7191	0.3775	14.777359	82.077242
2459513	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.451119	5.988818	19.983559	21.139731	34.835284	62.740150	19.007034	22.766468	0.8591	0.8886	0.2618	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.458070	-1.452143	0.437428	0.861796	0.672177	1.453067	0.145864	0.805597	0.9138	0.9586	0.1719	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.521477	-0.859148	-0.393534	0.771031	2.069665	0.666108	-0.532214	-0.254379	0.8931	0.8703	0.1777	6.624800	4.828285
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.434559	-0.470016	1.831804	2.038272	2.457716	5.275466	-0.108205	0.099731	0.8674	0.8651	0.4338	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	0.831775	-0.167911	-0.447314	-0.961830	0.016323	1.240593	-0.886782	0.398405	0.8631	0.9039	0.3566	0.000000	0.000000
2459500	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.157445	-0.148751	-0.595486	-0.863230	-0.157815	-0.979189	-1.011804	0.102160	0.8706	0.8989	0.3501	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	0.137140	-1.056639	-1.080682	-1.282574	-0.413293	-0.845226	-0.616924	0.464226	0.8843	0.9419	0.1740	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.185076	-1.061118	-0.963184	-1.100916	-0.594548	-0.643239	-0.494640	-0.508403	0.8779	0.8997	0.3808	0.000000	0.000000
2459497	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	0.162145	-1.503937	-0.992295	-1.100011	-0.614340	-0.599734	-0.813834	-0.165922	0.8942	0.9329	0.3094	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.043478	-1.282730	-0.742252	-0.877977	-0.774645	-0.609427	-0.793378	-0.501758	0.9108	0.9199	0.3549	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.015756	-1.143091	-0.694366	-0.954375	-0.478553	-0.626729	-0.830730	-0.243780	0.9070	0.9151	0.3931	0.000000	0.000000
2459494	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	-0.221179	-1.120214	-1.046845	-1.004868	-0.617555	-0.697117	-0.341347	-0.535389	0.8960	0.9077	0.4053	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	0.590730	-1.409790	1.569910	1.841883	0.256956	0.878529	0.296809	0.276896	0.7523	0.8107	0.3187	0.158424	0.165662
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	1.076951	-0.651407	2.215849	2.708902	-0.288724	2.393053	-0.667762	0.021996	0.7900	0.8237	0.2650	0.638520	0.602457
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	0.282728	-1.543698	2.138162	2.248354	0.198338	0.495058	-0.064024	0.129449	0.7421	0.8055	0.3711	1.633857	1.538025
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	1.094675	-1.090838	1.664952	2.213302	-0.300498	1.167581	-0.023468	0.148945	0.7589	0.8102	0.3220	1.713600	1.591326
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	14.29%	1.376255	0.050631	1.381486	1.684688	-0.900939	0.370564	0.026589	0.370893	0.7583	0.8133	0.3442	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 129 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 129, Node 10:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶